Power system load prediction method using fuzzy decision-based neural network model

A neural network model, fuzzy decision-making technology, applied in fuzzy logic-based systems, biological neural network models, prediction and other directions, can solve problems such as unsatisfactory accuracy and large power load error

CN105631532AInactive Publication Date: 2016-06-01STATE GRID JIANGSU ELECTRIC POWER CO LTD MAINTENANCE BRANCH +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2016-06-01
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses a power system load prediction method using a fuzzy decision-based neural network model. The method includes the following steps that: the degree of membership mui of historical load samples is obtained by using the fuzzy decision-based neural network model, and a historical sample membership degree curve is drawn; historical sample data of which the degree of membership is smaller than mui are removed, processing is performed, and therefore, samples which contain missing data and data that vary sharply can be basically eliminated; and after data pre-processing, an appropriate neural network model is selected, and change rules are found out based on the historical data of power load, and a power load neural network prediction model is established. According to the power system load prediction method using the fuzzy decision-based neural network model of the invention, the advantages of fuzzy decision and neural networks are combined, so that the convergence precision and convergence speed of the method can be both improved, and dependence on an initial value can be decreased, and therefore, the accuracy of prediction can be improved.
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Description

technical field

[0001] The invention relates to a fuzzy decision-based neural network model power system load forecasting method. Background technique

[0002] At present, the change of power load is mainly governed by people's production and life rules and presents regularity, and is affected by weather and other factors. The total load of an area is the sum of individual loads that are difficult to count, so there must be random components in the load. The periodicity and randomness of load changes are a pair of contradictions. The fluctuation between the two determines the predictability of the load and is an important factor affecting the accuracy of the load forecast. Improving the accuracy of load forecasting is the goal pursued by all researchers engaged in load forecasting, but the unpredictable forecasting error has always troubled many researchers. In fact, the historical load data used for modeling, the error of the model itself, There will be some internal lin...

Examples

Embodiment Construction

[0044] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

[0045] A neural network model power system load forecasting method based on fuzzy decision-making is characterized in that it comprises the following steps,

[0046] Step 1: Based on the neural network model of fuzzy decision-making, get the membership degree μ of all historical load samples i , to make a historical sample membership change curve.

[0047] Wherein step 1 includes the following steps:

[0048] Step 1.1: Calculate fuzzy positive and negative ideals Transform the given historical load data into triangular fuzzy numbers to obtain the matrix with Assuming that all indicators have equal weights, in Corresponding to n fuzzy index values, denoted as x...